Incode’s Login.gov contract shows why digital identity verification is tightening

At a glance

What this is: Incode reports a $37.4 million GSA award tied to Login.gov's next-generation identity proofing programme, with the core aim of improving verification accuracy while reducing fraud.

Why it matters: For IAM and fraud teams, this shows public-sector digital identity is moving toward stronger proofing that must still balance usability, privacy, and scale.

By the numbers:

• The contract award is worth $37.4 million.

Context

Digital identity verification is the process of checking that a person is who they claim to be before granting access. In public-sector services, that control must do three things at once: keep fraud out, avoid blocking legitimate users, and respect privacy constraints that are stricter than many commercial onboarding flows.

This article is really about the tightening of assurance requirements around Login.gov rather than a single procurement event. The identity angle is direct: when government access depends on remote proofing, the controls around biometric verification, fraud resistance, and account trust become part of the access model itself, not a separate back-office function.

Key questions

Q: How should public-sector teams govern mobile-first identity proofing?

A: Public-sector teams should govern mobile-first proofing as a risk-based assurance process, not a simple digital form. They need clear evidence thresholds, step-up review for weak captures, and policy rules for when biometric or document mismatch must trigger manual verification. Accessibility matters, but it cannot replace trust evidence.

Q: Why does remote eKYC increase the risk of identity fraud compared with face-to-face checks?

A: Remote eKYC raises risk because the verifier no longer controls the capture environment. A customer can submit images from a phone, scanner, webcam, or manipulated file, and the business must rely on software to detect whether the document and selfie are genuine. That shift weakens the physical assurance found in branch-based onboarding and makes fraud prevention depend on layered digital controls.

Q: What are the biggest mistakes teams make in identity proofing programmes?

A: The most common mistakes are over-collecting personal data, applying the same proofing level to every use case, and failing to connect proofing results to downstream access decisions. That creates privacy risk, poor user experience and weak governance because the verification outcome is not tied to a clear security decision.

Q: How do identity verification and fraud prevention work together in digital services?

A: Identity verification establishes who is entering the service, while fraud prevention checks whether the session, device, or transaction looks legitimate. In mature programmes, both share telemetry and escalation paths so suspicious behaviour can be challenged before an account takeover or payment loss is completed.

Technical breakdown

Remote identity proofing shifts trust to signal quality

Remote unsupervised identity proofing asks a system to establish trust without a live agent or in-person review. That moves the security problem to signal quality: document checks, liveness detection, biometric matching, device and network risk, and fraud scoring all have to compensate for the lack of human oversight. The more remote the flow, the more important it becomes to correlate multiple weak signals rather than rely on any single check. In government contexts, that also means designing for accessibility and false reject rates, not just fraud suppression.

Practical implication: evaluate whether your proofing flow combines enough independent signals to resist spoofing without creating unacceptable friction for legitimate users.

Why biometric identity platforms become policy controls

A biometric platform in this context is not just a verification tool. It becomes a policy control because it influences who can enroll, which evidence is accepted, how exceptions are handled, and when a user is allowed to proceed. That means governance has to cover threshold tuning, fallback paths, exception handling, and privacy protections alongside the model itself. In a public programme, those decisions shape both fraud exposure and citizen experience. The core question is no longer whether biometrics are used, but how tightly they are bound to assurance policy.

Practical implication: treat biometric decisioning as an identity governance control and review its thresholds, overrides, and fallbacks as part of access policy.

Fraud prevention and privacy now have to coexist

Identity systems often fail when privacy and security are treated as trade-offs instead of design constraints. In this article, the stated goal is to improve fraud resistance while protecting personal information, which is exactly the governance tension public-sector identity programmes must manage. Stronger identity proofing can collect more data, but better architecture should minimise retention, limit reuse, and narrow who can see sensitive verification evidence. That is especially important when the same identity layer serves many services and populations with different risk profiles.

Practical implication: design identity proofing so that fraud controls, data minimisation, and retention limits are governed together rather than as separate workstreams.

NHI Mgmt Group analysis

Digital identity proofing is becoming a governance control, not just an onboarding step. When government services rely on remote verification, the proofing decision directly shapes downstream access risk, fraud exposure, and user trust. That makes identity assurance part of the control plane for public-service access, not a peripheral verification task. Practitioners should govern proofing as an access policy with measurable assurance thresholds.

Privacy-first identity design is now a fraud control requirement. Public-sector programmes cannot simply add more data collection and call it stronger security. The harder problem is to use only the evidence needed to establish trust, retain it for as little time as possible, and prevent cross-service reuse from turning verification artefacts into a broader privacy liability. Practitioners should align proofing design with data minimisation and retention governance.

Login.gov-style assurance illustrates the verification trust gap. Remote systems must decide whether a claim is trustworthy without the human cues that in-person processes used to rely on. That gap is where synthetic identities, deepfake-assisted enrolment, and replayed evidence can succeed if controls are thin. Practitioners should assume trust must be rebuilt from multiple signals, not inherited from a single document check.

Identity security and fraud prevention are converging around the same control decisions. The article shows that public-sector identity programmes are being asked to do both jobs at once. That convergence is forcing teams to re-evaluate proofing policy, exception handling, and user journey design together rather than in separate governance tracks. Practitioners should prepare for more integrated identity and fraud oversight.

Biometric assurance needs explicit accountability. Once biometric or remote proofing systems are used to decide access to government services, the organisation must be able to explain why a person was accepted, rejected, or escalated. That means auditable thresholds, documented fallback routes, and reviewable exception handling. Practitioners should make proofing outcomes defensible under operational and regulatory scrutiny.

From our research library:

• Nearly 60% of companies reported that fraud losses were still increasing in 2025.

What this signals

Verification trust gap: public-sector identity programmes now have to prove trust without relying on a live human verifier. That shifts the burden onto signal fusion, fallback governance, and privacy-aware evidence handling, which is where many programmes are still immature.

The practitioner lesson is that identity proofing should be governed as a dynamic policy layer. When the proofing decision is disconnected from downstream access, the organisation can improve verification and still leave account trust poorly controlled.

For practitioners

• Define assurance thresholds for every identity journey Set explicit acceptance, step-up, and fallback criteria for remote proofing so every service uses the same risk logic.
• Minimise biometric and identity evidence retention Limit storage of documents, selfie data, and decision artefacts to what is required for fraud detection, audit, and legal retention.
• Test exception handling and recovery paths Exercise the flows used when proofing fails, including manual review, alternate evidence, and recovery after suspected fraud.
• Tie proofing policy to access governance Ensure that identity proofing outcomes feed downstream access policy, rather than sitting as a disconnected enrolment record.

Key takeaways

• Remote identity proofing is becoming a core governance control for public-sector access, not a back-office onboarding step.
• The main risk is not verification alone but the combination of fraud resistance, accessibility, and privacy in one journey.
• Teams should govern proofing thresholds, retention, and exception handling as part of the access model itself.

Key terms

• Remote Identity Proofing: Remote identity proofing is the process of verifying that a person is who they claim to be without meeting in person. It combines document checks, biometric comparison, device and signal analysis, and fraud screening to establish confidence in identity remotely. It is used before account creation, recovery, or high-risk access.
• Identity Assurance: The confidence an organisation has that a person or system is truly who it claims to be before access or action is granted. In modern IAM, assurance depends on evidence quality, channel trust, and the strength of verification around high-risk decisions.
• Biometric Decisioning: Biometric decisioning is the policy layer that determines how biometric signals are used in verification or authentication. It covers thresholds, fallbacks, exception handling, and auditability, so the biometric check is governed as part of an identity control rather than as a standalone model.
• Phishing-Resistant Identity Verification: Phishing-resistant identity verification is a method that does not rely on shared secrets, one-time passcodes, or knowledge questions that attackers can easily intercept or guess. It uses stronger evidence, often including device-bound or biometric checks, to confirm the person requesting help is the legitimate user. This reduces social engineering risk.

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